Github user junyangq commented on a diff in the pull request:
https://github.com/apache/spark/pull/14980#discussion_r78679227
--- Diff: R/pkg/vignettes/sparkr-vignettes.Rmd ---
@@ -385,22 +385,29 @@ head(result[order(result$max_mpg, decreasing = TRUE),
])
Similar to `lapply` in native R, `spark.lapply` runs a function over a
list of elements and distributes the computations with Spark. `spark.lapply`
works in a manner that is similar to `doParallel` or `lapply` to elements of a
list. The results of all the computations should fit in a single machine. If
that is not the case you can do something like `df <- createDataFrame(list)`
and then use `dapply`.
+We use `svm` in package `e1071` as an example. We use all default settings
except for varying costs of constraints violation. `spark.lapply` can train
those different models in parallel.
+
```{r}
-families <- c("gaussian", "poisson")
-train <- function(family) {
- model <- glm(mpg ~ hp, mtcars, family = family)
+costs <- exp(seq(from = log(1), to = log(1000), length.out = 5))
--- End diff --
It runs as long as `e1071` is installed in the workers. Perhaps it's better
to add a check there?
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